Biology subjects

Robbani, S. M.

Publications and source records attributed to Robbani, S. M..

7 recordsLinked to original sources

bioRxiv2026

Planetary structure and drivers of diazotroph communities reveal key reservoirs of nitrogen-fixation potential

Biological nitrogen fixation sustains productivity across Earths ecosystems, yet the diversity, distribution and ecological drivers of nitrogen-fixing prokaryotes (diazotrophs) remain incompletely resolved. Here we screened approximately 40 billion genes to generate a catalogue of 21,157 diazotroph genomes. Using this catalogue, we quantified diazotroph diversity and abundance across 9,163 metagenomes spanning diverse environments. Diazotrophy was distributed across the prokaryotic tree of life, but both gene diversity and metagenomic abundance were dominated by heterotrophic lineages. Freshwater and sedimentary ecosystems emerged as major reservoirs of nitrogen-fixation potential beyond the environments that have historically anchored the field. Diazotroph-rich assemblages were associated with nitrogen and phosphorus limitation together with carbohydrate-rich metabolic regimes, revealing distinct nutrient and energy niches for cyanobacterial and heterotrophic diazotrophs. Together, this framework unifies disparate views of diazotroph diversity, abundance and ecology, providing a foundation for improved prediction of biological nitrogen fixation across Earths ecosystems.

microbiology↗

bioRxiv2025

Planetary microbiome structure and generalist-driven gene flow across disparate habitats

Microbes are ubiquitous on Earth, forming microbiomes that sustain macroscopic life and biogeochemical cycles. Microbial dispersion, driven by natural processes and human activities, interconnects microbiomes across habitats, yet most comparative studies focused on specific ecosystems. To study planetary microbiome structure, function, and inter-habitat interactions, we systematically integrated 85,604 public metagenomes spanning diverse habitats worldwide. Using species-based unsupervised clustering and parameter modeling, we delineated 40 habitat clusters and quantified their ecological similarity. Our framework identified key drivers shaping microbiome structure, such as ocean temperature and host lifestyle. Regardless of biogeography, microbiomes were structured primarily by host-associated or environmental conditions, also reflected in genomic and functional traits inferred from 2,065,975 genomes. Generalists emerged as vehicles thriving and facilitating gene flow across ecologically disparate habitat types, illustrated by generalist-mediated horizontal transfer of an antibiotic resistance island across human gut and wastewater, further dispersing to other habitats, exemplifying human impact on the planetary microbiome.

microbiology↗

bioRxiv2024

Refined Enterotyping Reveals Dysbiosis in Global Fecal Metagenomes

BackgroundEnterotypes describe human fecal microbiomes grouped by similarity into clusters of microbial community composition, often associated with disease, medications, diet, and lifestyle. Numbers and determinants of enterotypes have been derived by diverse frameworks and applied to cohorts that often lack diversity or inter-cohort comparability. ResultsTo overcome these limitations, we selected 16,772 fecal metagenomes collected from 38 countries to revisit the enterotypes using state-of-the-art fuzzy clustering and found robust clustering regardless of underlying taxonomy, consistent with previous findings. Quantifying the strength of enterotype classifications enriched the enterotype landscape, also reflecting some continuity of microbial compositions. As the classification strength was associated with the patients health status, we established an "Enterotype Dysbiosis Score" (EDS) as a latent covariate for various diseases. ConclusionThis global study confirms the enterotypes, reveals a dysbiosis signal within the enterotype landscape, and enables robust classification of metagenomes with an online "Enterotyper" tool, allowing reproducible analysis in future studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/607711v3_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1611a2org.highwire.dtl.DTLVardef@dfbb57org.highwire.dtl.DTLVardef@848ac0org.highwire.dtl.DTLVardef@1b15808_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ both in relative composition and absolute abundance. While sequencing approaches determine only the relative abundances of taxa and genes, experimental techniques for absolute abundance determination are rarely applied to large-scale microbiome studies. Here, we developed a machine learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applied to large-scale datasets (n = 34,539), we demonstrate that microbial load is the major determinant of gut microbiome variation and associated with numerous host factors. We found that for several diseases, the altered microbial load, not the disease itself, was the main driver of the gut microbiome changes. Adjusting for this effect substantially reduced the significance of more than half of the disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

microbiology↗

bioRxiv2020

PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles

MotivationProtein dynamic is essential for cellular functions. Due to the complex nature of non-covalent interactions and their long-range effects, the analysis of protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) rely on different philosophies, and the currently available tools suffer from limitations in terms of input formats, supported network models, and version control. Another issue is the precise definition of cutoffs for the network calculations and the assessment of the stability of the parameters, which ultimately affect the outcome of the analyses. ResultsWe provide two open-source software packages, i.e., PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a harmonized, reproducible, and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and calculates a diverse range of network models with the possibility to integrate them into a macro-network and perform further downstream graph analyses. PyInKnife2 is a standalone package that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. Several functionalities are based on MDAnalysis and NetworkX, including parallelization, and are available for Python 3.7. PyInteraph2 underwent a massive restructuring in terms of setup, installation, and test support compared to the original PyInteraph software. ConclusionsWe foresee that the modular structure of the code and the version control system of GitHub will promote the transition to a community-driven effort, boost reproducibility, and establish harmonized protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and maintenance of the package. AvailabilityThe packages are available at https://github.com/ELELAB/pyinteraph2 and https://github.com/ELELAB/PyInKnife2 with guides provided within the packages.

bioinformatics↗

bioRxiv2019

Profiling Gene Specific Changes to RNA Editome in Preeclampsia

RNA editing is a form of post-transcriptional modification that results in changes to the messenger RNA sequence. At the onset of the study we focused on detecting the changes in RNA editing patterns in cell lines exposed to hypoxic conditions followed by the detection of changes in RNA editing patterns in the fetuses of preeclamptic mothers using publicly available RNA sequence data from the NCBI SRA database. The results showed an increase in RNA editing activity in hypoxic cell lines and a decrease in RNA editing activity in the fetuses with preeclamptic mothers. A total of 85 genes common in the cell lines and 33 in the fetus disease models and not present in controls were identified as harboring editing sites in exonic, downstream, upstream or splicing regions. Subsequently we focused on unique editing sites in genes and categorized in order of relevance to Preeclampsia as A, B and C (A being most closely related to the disease and C the least). The genes implicated ones involved in respiration chains, blood cell growth, cytokine and complement activation. Among the most significant of the genes were CTSB, GSR, CASP10, and MAPK13. Total number of common editing sites were found in different conditions and these were 667 for cell lines and 23 for fetuses. Validation of these variations in a larger samples size determines refined editing sites which could be used as potential diagnostic markers for intervention.

genomics↗

bioRxiv2018

Identifying Changes in RNA Editome Unique to Major Depressive Disorder

Major Depressive Disorder (MDD) is one of the most significant psychiatric disorders in the world today. Its incidence is widespread in society and its heavy adverse impact on the quality of life is well documented. Previously genetic studies on MDD had identified a hereditary component of the disease as well as crediting RNA editing with a role in its development. The later due to an overexpression of a heavily edited isoform of the Serotonin 2c receptor. Here we used publicly available RNA sequence data from suicide patients diagnosed with MDD as well as controls for identifying RNA editing sites unique to MDD. After variant calling and several steps of filtering, we identified 142 unique RNA editing sites in the MDD patients. These included intronic, downstream, UTR3 and exonic edits. The latter comprising several amino acid changes in the encoded protein. The genes implicated to be uniquely edited in MDD included the aforementioned and previously implicated Serotonin 2c receptor, others involved in functions that play roles in depression and suicide such as Cannabinoid Receptor 1, Frizzled Class 3 Receptor, Neuroligin 3 and others.

genomics↗